Results in the Constitution appear in the center column; results from essays appear in the right column. Use a short, descriptive search phrase. Precise phrases like âreligious freedom,â âtaxation,â or âwomenâs suffrageâ will work better than longer phrases.
Multicollinearity refers to a situation in which two or more explanatory variables in a multiple regression model are highly linearly related. We have perfect multicollinearity if, for example as in the equation above, the correlation between two independent variables is equal to 1 or -1.In practice, we rarely face perfect multicollinearity in a data set.rom Wikipedia, the free encyclopedia. In statistics, multicollinearity (also collinearity) is a phenomenon in which two or more predictor variables in a multiple regression model are highly correlated, meaning that one can be linearly predicted from the others with a substantial degree of accuracy.
If multicollinearity is a problem in your model -- if the VIF for a factor is near or above 5 -- the solution may be relatively simple.Try one of these: 1  Remove highly correlated predictors from the model. 2  If you have two or more factors with a high VIF, remove one from the model.n regression, multicollinearity refers to predictors that are correlated with other predictors. Multicollinearity occurs when your model includes multiple factors that are correlated not just to your response variable, but also to each other.